It begins with a sense of magic. You type a simple sentence like “A child building a sandcastle on the moon” and within seconds you get a mesmerizing, richly detailed image that looks as if it was captured by a space photographer with access to a Disney studio. You’re enchanted. The future is here. But then you ask the AI to illustrate your grandmother celebrating her 90th birthday, and discover she’s holding two glasses of wine, has three hands, and an eye located on her forehead. Suddenly, the future seems less like a utopia and more like a Dali painting after a sleepless night.
Over the past decade, artificial intelligence has evolved from a toy to a powerhouse. It writes, translates, analyzes, describes, recommends, argues — and in some ways even mimics consciousness. But beneath the digital sparkle lurk glitches, distortions, and sometimes fundamental misunderstandings. The more advanced the systems become, the more we notice those moments when they are ridiculous, stammering, or just plain wrong. Welcome to the new reality: The computer may know everything, but it still understands nothing.
Anatomy According to a Robot: Six Fingers and an Eye on the Forehead
AI-based image generators like Midjourney and DALL-E offer a sophisticated display of digital creativity — until they encounter the oldest challenge in the world: The human body. The results range from amusing to disturbing. Hands with six or seven fingers, eyes of different sizes, teeth in industrial quantities, and faces that morph from frame to frame. The algorithms learn from millions of images, but they have no real concept of anatomy or physiology. When coherence is required — for example, creating a series of images of the same character — they simply fail to maintain basic consistency.
These errors also extend to the laws of physics: Floating objects, cups melting into tables, light falling from impossible directions, and shadows that simply don’t exist. And if you ask for embedded Hebrew text, you’ll likely get blurry letters, an unrecognizable mix of Hebrew and English, and a total misunderstanding of right-to-left direction. This isn’t a design flaw — it’s a deep perceptual failure.
Fabrications, Errors, and Outdated Knowledge
One of the most talked-about flaws of language models like ChatGPT and Gemini is their tendency to invent. They make up articles, interview nonexistent people, fabricate names of imaginary professors, and attach dates, sources, and quotes — all quietly and with an authoritative tone. The issue becomes even more serious with current events: Since the models are limited to information frozen at a specific point in time, they are unaware of anything that happened afterward. Questions about politics, pandemics, science, or even new technology can result in answers disconnected from reality.
Beyond outdated information, there’s also a deeper problem: Inconsistency, logical jumps, forgotten details from previous conversations, basic calculation errors, and summaries that distort the writer’s intent. A system that can understand sentence structure still doesn’t understand ideas — and that’s a crucial distinction.
Irony Is Still an Inside Joke
AI can read poetry and respond in verse, but humor? That’s where the real misfires occur. The ability to grasp sarcasm, nuance, puns, or hidden intonation is still lacking. Even double negatives or subtle hints are often met with solemn seriousness — and sometimes comical failure.
This problem is also apparent in translation. Despite impressive improvements, machine translation still suffers from being overly literal, using phrases that don’t exist in the target language, and syntax that reflects the source language more than the natural flow of the translated one.
Excessive Niceness, Lack of Objectivity, and Generic Answers
To avoid legal, political, or moral entanglements, many AI systems are programmed to be agreeable and even flattering. But this attempt to make them harmless often comes at the cost of value. Ask the system for an opinion on a controversial topic, and you’ll get a response that carefully avoids taking any stance. Ask for a critique, and you’ll get encouragement. Pose an open-ended question — and the model will often rephrase the question instead of answering it.
This “niceness” also translates into the production of content that is generic, superficial, and alarmingly uniform in tone. It often feels like the answers are written by a public relations officer obsessed with political correctness. The ability to think outside the box exists only when explicitly requested — and even then, only within the pre-defined boundaries set for it.
So Why Does This Happen?
The errors aren’t random — they are a direct result of how AI systems operate. First, they don’t “understand” the world, but rather mimic it through probabilities learned from millions of examples. They have no conceptual grasp of meaning, context, morality, or anatomy — only statistical likelihoods of what tends to appear next to what. Second, the output depends directly on the quality and precision of the prompt. A prompt that’s too general will yield a vague result; one that’s too complex may lead to confusion.
Moreover, the models rely on static data sets — meaning they are not connected to the internet in real time and are not updated with events that occurred after their data was frozen. This is a fundamental limitation that affects the accuracy and reliability of the information they provide. Biases from training materials, multilingual data, inconsistent translations, or just statistical errors — all of these are additional factors that can lead to incorrect, inconsistent, and sometimes outright absurd results.
These mistakes are not signs of failure — but of a process. Artificial intelligence is not consciousness, but a tool. And it has a clear role: To enhance human capabilities, not replace them. The malfunctions, flaws, and blunders of AI are not signs of technological weakness — but reminders of the depth of human complexity.
So next time the model offers to draft a birthday greeting and instead writes a business proposal with grammar mistakes, or you ask for a family portrait and get a horror show — smile. The computer, it seems, is just trying to understand humans. In the meantime, we recommend carefully reviewing your AI results, cross-checking the information, and taking the output with a grain of salt.